Top

Neural Computing and Applications

Published in:

13-04-2018 | Original Article

Face sketch recognition using a hybrid optimization model

Authors: Hussein Samma, Shahrel Azmin Suandi, Junita Mohamad-Saleh

Published in: Neural Computing and Applications | Issue 10/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this work, a hybrid optimization-based model is introduced to handle the problem of face sketch recognition. The proposed model comprises a total of three layers that are global search layer, control layer, and fine-tuning layer. The global layer contains a set of search operations from particle swarm optimization (PSO) algorithm to perform the task of global search. However, the control layer is responsible about controlling the execution of the implemented search operations at run time. Finally, the fine-tuning layer is aimed at performing search refinement to enhance the search ability. For sketch recognition, the proposed hybrid model is applied on the input face sketch to locate the internal sketch facial components. Three types of texture features extraction techniques are adopted in this study including Histogram Of Gradient (HOG), Local Binary Pattern (LBP), and Gabor wavelet. To assess the performances of the proposed model, a total of three face sketch databases have been used which are LFW, AR, and CUHK. The reported results indicate that the proposed hybrid model was able to achieve a competitive performance with 96% on AR, 87.68% on CUHK, and 50.00% on LFW. Additionally, the outcomes reveal that the proposed model statistically outperforms others PSO-based models as well as the state-of-the-art meta-heuristic optimization models.

previous article Hopfield network-based approach to detect seam-carved images and identify tampered regions

next article Exploiting label consistency in structured sparse representation for classification

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Peng C, Gao X, Wang N, Li J (2017) Superpixel-based face sketch–photo synthesis. IEEE Trans Circuits Syst Video Technol 27(2):288–299CrossRef

Gao X, Zhong J, Tao D, Li X (2008) Local face sketch synthesis learning. Neurocomputing 71(10–12):1921–1930CrossRef

Zhang S, Gao X, Wang N, Li J (2017) Face sketch synthesis from a single photo–sketch pair. IEEE Trans Circuits Syst Video Technol 27(2):275–287CrossRef

Tu C-T, Chan Y-H, Chen Y-C (2016) Facial sketch synthesis using 2D direct combined model-based face-specific markov network. IEEE Trans Image Process 25:3546–3561MathSciNetMATHCrossRef

Wang N, Li J, Tao D, Li X, Gao X (2013) Heterogeneous image transformation. Pattern Recognit Lett 34:77–84CrossRef

Bhatt HS, Bharadwaj S, Singh R, Vatsa M (2012) Memetically optimized MCWLD for matching sketches with digital face images. IEEE Trans Inf Forensics Secur 7:1522–1535CrossRef

Hu H, Klare BF, Bonnen K, Jain AK (2013) Matching composite sketches to face photos: a component-based approach. IEEE Trans Inf Forensics Secur 8:191–204CrossRef

Klare BF, Zhifeng L, Jain AK (2011) Matching forensic sketches to mug shot photos. IEEE Trans Pattern Anal Mach Intell 33:639–646CrossRef

Xiaoou T, Xiaogang W (2004) Face sketch recognition. IEEE Trans Circuits Syst Video Technol 14:50–57CrossRef

10.

Bhatt HS, Bharadwaj S, Singh R, Vatsa M (2010) On matching sketches with digital face images, biometrics: theory applications and systems (BTAS). In: 2010 Fourth international conference on IEEE, pp 1–7

11.

Samma H, Lim CP, Saleh JM (2016) A new reinforcement learning-based memetic particle swarm optimizer. Appl Soft Comput 43:276–297CrossRef

12.

Wu Z, Wu Z, Zhang J (2016) An improved FCM algorithm with adaptive weights based on SA-PSO. Neural Comput Appl 28:1–6CrossRef

13.

Samma H, Lim CP, Saleh JM, Suandi SA (2016) A memetic-based fuzzy support vector machine model and its application to license plate recognition. Memetic Comput 8:1–17CrossRef

14.

Abd-Elazim S, Ali E (2013) A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Int J Electr Power Energy Syst 46:334–341CrossRef

15.

Abd-Elazim S, Ali E (2013) Synergy of particle swarm optimization and bacterial foraging for TCSC damping controller design. Int J WSEAS Trans Power Syst 8:74–84

16.

Abd-Elazim S, Ali E (2013) Power system stability enhancement via bacteria foraging optimization algorithm, Arabian Journal for Science & Engineering, vol 38. Springer, Berlin

17.

Kamboj VK (2016) A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput Appl 27:1643–1655CrossRef

18.

Subasi A, Kevric J, Canbaz MA (2017) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 1–9. https://doi.org/10.1007/s00521-017-3003-y

19.

Sun S, Li J (2014) A two-swarm cooperative particle swarms optimization. Swarm Evolut Comput 15:1–18CrossRef

20.

Lim WH, Mat Isa NA (2013) Two-layer particle swarm optimization with intelligent division of labor. Eng Appl Artif Intel 26:2327–2348CrossRef

21.

Lim WH, Mat Isa NA (2014) Teaching and peer-learning particle swarm optimization. Appl Soft Comput 18:39–58CrossRef

22.

Lim WH, Mat Isa NA (2014) Particle swarm optimization with increasing topology connectivity. Eng Appl Artif Intel 27:80–102CrossRef

23.

Li X (2010) Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans Evolut Comput 14:150–169CrossRef

24.

Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8:204–210CrossRef

25.

de Oca MAM, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evolut Comput 13:1120–1132CrossRef

26.

Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10:281–295CrossRef

27.

Zhi-Hui Z, Jun Z, Yun L, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39:1362–1381CrossRef

28.

Hu M, Wu T, Weir JD (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evolut Comput 17:705–720CrossRef

29.

Zhang W, Ma D, Wei J-J, Liang H-F (2014) A parameter selection strategy for particle swarm optimization based on particle positions. Expert Syst Appl 41:3576–3584CrossRef

30.

Çomak E (2016) A modified particle swarm optimization algorithm using Renyi entropy-based clustering. Neural Comput Appl 5:1381–1390CrossRef

31.

Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31:1955–1967CrossRef

32.

Zhou H, Kuang Z, Wong K.-Y.K (2012) Markov weight fields for face sketch synthesis, computer vision and pattern recognition (CVPR). In: 2012 IEEE Conference on IEEE, pp 1091–1097

33.

Wang N, Tao D, Gao X, Li X, Li J (2013) Transductive face sketch-photo synthesis. IEEE Trans Neural Netw Learn Syst 24:1364–1376CrossRef

34.

Peng C, Gao X, Wang N, Li J (2017) Superpixel-based face sketch-photo synthesis. IEEE Trans Circuits Syst Video Technol 27:288–299CrossRef

35.

Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34:2274–2282CrossRef

36.

Wang N, Gao X, Sun L, Li J (2017) Bayesian face sketch synthesis. IEEE Trans Image Process 26:1264–1274MathSciNetMATHCrossRef

37.

Zhang Y, McCullough C, Sullins JR, Ross CR (2010) Hand-drawn face sketch recognition by humans and a pca-based algorithm for forensic applications. IEEE Trans Syst Man Cyber Part A Syst Humans 40:475–485CrossRef

38.

Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35:1410–1422CrossRef

39.

Lahasan BM, Venkat I, Al-Betar MA, Lutfi SL, De Wilde P (2016) Recognizing faces prone to occlusions and common variations using optimal face subgraphs. Appl Math Comput 283:316–332MathSciNetMATH

40.

Lahasan BM, Venkat I, Lutfi SL (2014) Recognition of occluded faces using an enhanced EBGM algorithm, computer and information sciences (ICCOINS). In: 2014 international conference on IEEE, pp 1–5

41.

Connolly J-F, Granger E, Sabourin R (2012) Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition. Pattern Recognit 45:2460–2477CrossRef

42.

Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675CrossRef

43.

Al-Ayyoub M, AlZu’bi S, Jararweh Y, Shehab MA, Gupta BB (2016) Accelerating 3D medical volume segmentation using GPUs. Multim Tools Appl 77:1–20

44.

Jararweh Y, Al-Ayyoub M, Fakirah M, Alawneh L, Gupta BB (2017) Improving the performance of the needleman–wunsch algorithm using parallelization and vectorization techniques. Multim Tools Appl 1–17. https://doi.org/10.1007/s11042-017-5092-0

45.

Alsmirat MA, Jararweh Y, Al-Ayyoub M, Shehab MA, Gupta BB (2017) Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU–GPU implementations. Multim Tools Appl 76:3537–3555CrossRef

46.

Kennedy J, Eberhart R (1995) Particle swarm optimization, Neural Networks. IEEE Int Conf Proc 1944:1942–1948

47.

Ratnaweera A, Halgamuge S, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evolut Comput 8:240–255CrossRef

48.

Van Laarhoven PJ, Aarts EH (1987) Simulated annealing, simulated annealing: theory and applications. Springer, Berlin, pp 7–15MATHCrossRef

49.

Déniz O, Bueno G, Salido J, De la Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recognit Lett 32:1598–1603CrossRef

50.

Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041MATHCrossRef

51.

Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11:467–476CrossRef

52.

Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst

53.

Martinez A, Benavente R (2007) The AR face database, 1998. Computer vision center, technical report, p 3

54.

Weiping C, Yongsheng G (2013) Face recognition using ensemble string matching. IEEE Trans Image Process 22:4798–4808MathSciNetMATHCrossRef

55.

Gao Y, Qi Y (2005) Robust visual similarity retrieval in single model face databases. Pattern Recognit 38:1009–1020CrossRef

56.

Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3:71–86CrossRef

57.

Efron B (1979) Bootstrap methods: another look at the Jackknife. Ann Stat 7:1–26MathSciNetMATHCrossRef

58.

Efrorn B, Tibshirani RJ (1993) An Introduction to the bootstrap. Chapman and Hall, New YorkCrossRef

59.

Van den Bergh F, Engelbrecht AP (2004) A Cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8:225–239CrossRef

60.

Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm. Technical note, Manufacturing Engineering Centre, Cardiff University, UK, pp 1–57

61.

Zhao SZ, Suganthan PN, Pan Q-K, Fatih M (2011) Tasgetiren, Dynamic multi-swarm particle swarm optimizer with harmony search. Exp Syst Appl 38:3735–3742CrossRef

62.

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef

63.

Mirjalili S, Mirjalili S, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl, pp 1–19

64.

Sahu A, Panigrahi SK, Pattnaik S (2012) Fast convergence particle swarm optimization for functions optimization. Proc Technol 4:319–324CrossRef

65.

Liu B, Wang L, Jin Y-H (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cyber Part B Cyber 37:18–27CrossRef

66.

Rakshit P, Konar A, Bhowmik P, Goswami I, Das S, Jain LC, Nagar AK (2013) Realization of an adaptive memetic algorithm using differential evolution and Q-learning: a case study in multirobot path planning. IEEE Trans Syst Man Cyber Part B Cyber 43:814–831CrossRef

Title: Face sketch recognition using a hybrid optimization model
Authors: Hussein Samma
Shahrel Azmin Suandi
Junita Mohamad-Saleh
Publication date: 13-04-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3475-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 10/2019

Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Application of radial basis functions in solving fuzzy integral equations

Off-line Bangla handwritten word recognition: a holistic approach

Bag-of-visual-words for signature-based multi-script document retrieval

Dynamical regularized echo state network for time series prediction

Robust collaborative representation-based classification via regularization of truncated total least squares

Premium Partner